Built on

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge. International journal of Computer Vision 88
2010
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)
Original
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of Computer Vision 115
2015
Earlier work this paper cites.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)
Original
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5mb model size. arXiv preprint arXiv:1602.07360 (2016)
Original
2016
Earlier work this paper cites.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Proceedings of European Conference on Computer Vision. pp. 21–37 (2016)
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: Proceedings of European Conference on Computer Vision. pp. 525–542 (2016)
2016
Earlier work this paper cites.
Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision. pp. 1–9 (2016)
2016
Earlier work this paper cites.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Original
2017
Earlier work this paper cites.

Similar

Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197 (2017)
Original
2017
Cited alongside, same era.
Migacz, S.: 8-bit inference with tensorrt. In: Proceedings of Conference on GPU Technology. p. 7 (2017)
2017
Cited alongside, same era.
Mishra, A., Nurvitadhi, E., Cook, J.J., Marr, D.: Wrpn: wide reduced-precision networks. arXiv preprint arXiv:1709.01134 (2017)
Original
2017
Cited alongside, same era.
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: Agedb: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 51–59 (2017)
2017
Cited alongside, same era.

Then

Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 (2018)
Original
2018
Later among the works it cites.
Zheng, T., Deng, W.: Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments. Technical Report 5
2018
Later among the works it cites.
Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.: Towards effective low-bitwidth convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7920–7928 (2018)
2018
Later among the works it cites.
Alibaba: MNN: a lightweight deep neural network inference engine. https://github.com/alibaba/MNN (2019)
2019
Later among the works it cites.
Choukroun, Y., Kravchik, E., Yang, F., Kisilev, P.: Low-bit quantization of neural networks for efficient inference. arXiv preprint arXiv:1902.06822 (2019)

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2020

EasyQuant: Post-training Quantization via Scale Optimization

Wu, Di, Tang, Qi, Zhao, Yongle et al.

Understand

The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications.

There are two kinds of quantization methods, training-based quantization and post-training quantization.
Training-based approach suffers from a cumbersome training process, while post-training quantization may lead to unacceptable accuracy drop.
In this paper, we present an efficient and simple post-training method via scale optimization, named EasyQuant (EQ),that could obtain comparable accuracy with the training-based method.Specifically, we first alternately optimize scales of weights and activations for all layers target at convolutional outputs to further obtain the high quantization precision.

Built on

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge. International journal of Computer Vision 88
2010
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)
Original
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of Computer Vision 115
2015
Earlier work this paper cites.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)
Original
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5mb model size. arXiv preprint arXiv:1602.07360 (2016)
Original
2016
Earlier work this paper cites.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Proceedings of European Conference on Computer Vision. pp. 21–37 (2016)
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: Proceedings of European Conference on Computer Vision. pp. 525–542 (2016)
2016
Earlier work this paper cites.
Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision. pp. 1–9 (2016)
2016
Earlier work this paper cites.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Original
2017
Earlier work this paper cites.

Similar

Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197 (2017)
Original
2017
Cited alongside, same era.
Migacz, S.: 8-bit inference with tensorrt. In: Proceedings of Conference on GPU Technology. p. 7 (2017)
2017
Cited alongside, same era.
Mishra, A., Nurvitadhi, E., Cook, J.J., Marr, D.: Wrpn: wide reduced-precision networks. arXiv preprint arXiv:1709.01134 (2017)
Original
2017
Cited alongside, same era.
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: Agedb: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 51–59 (2017)
2017
Cited alongside, same era.

Then

Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 (2018)
Original
2018
Later among the works it cites.
Zheng, T., Deng, W.: Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments. Technical Report 5
2018
Later among the works it cites.
Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.: Towards effective low-bitwidth convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7920–7928 (2018)
2018
Later among the works it cites.
Alibaba: MNN: a lightweight deep neural network inference engine. https://github.com/alibaba/MNN (2019)
2019
Later among the works it cites.
Choukroun, Y., Kravchik, E., Yang, F., Kisilev, P.: Low-bit quantization of neural networks for efficient inference. arXiv preprint arXiv:1902.06822 (2019)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Tencent: NCNN: a high-performance neural network inference framework optimized for the mobile platform. https://github.com/Tencent/ncnn (2017)

2017

Cited alongside, same era.

Zheng, T., Deng, W., Hu, J.: Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197 (2017)

Original

2017

Cited alongside, same era.

Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: Towards lossless cnns with low-precision weights. In: Proceedings of International Conference on Learning Representations (2017)

2017

Cited alongside, same era.

Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. In: Proceedings of International Conference on Learning Representations (2017)

2017

Cited alongside, same era.

Banner, R., Nahshan, Y., Soudry, D.: Post-training 4-bit quantization of convolution networks for rapid-deployment. arXiv preprint arXiv:1810.05723 (2018)

Original

2018

Cited alongside, same era.

Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: A dataset for recognising faces across pose and age. In: Proceedings of IEEE International Conference on Automatic Face Gesture Recognition. pp. 67–74 (2018)

2018

Cited alongside, same era.

Choi, J., Wang, Z., Venkataramani, S., Chuang, P.I.J., Srinivasan, V., Gopalakrishnan, K.: Pact: Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018)

Original

2018

Cited alongside, same era.

Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2704–2713 (2018)

2018

Cited alongside, same era.

Original

2019